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Article summary:
1. This article examines the ability of three machine learning models (M5-Tree, extreme learning machine, and random forest) to predict shear strength (Vs) of FRP reinforced concrete beams with transverse reinforcement.
2. Statistical evaluation and graphical approaches were used to evaluate the efficiency of the proposed models, which showed that M5 tree model using nine input parameters had the best prediction performance with a coefficient of determination (R2) and root mean square error (RMSE) equal to 0.9313 and 35.5083 KN, respectively.
3. The article contributes to basic knowledge of investigating the impact of stirrups on Vs of FRP reinforced concrete beam with the potential of applying different computer aid models.
Article analysis:
The article is generally well written and provides a comprehensive overview of the research conducted by the authors in order to develop machine learning models for predicting shear strength (Vs) of FRP reinforced concrete beams with transverse reinforcement. The authors provide a thorough review of previous studies related to this topic, as well as an extensive description of their dataset and methodology used in their research.
However, there are some areas where the article could be improved upon in terms of trustworthiness and reliability. For example, while the authors provide an extensive review of previous studies related to this topic, they do not explore any counterarguments or alternative perspectives that may exist in these studies. Additionally, while they provide a detailed description of their dataset and methodology used in their research, they do not discuss any potential biases or sources for these biases that may exist within their data or methods. Furthermore, while they present their results in detail, they do not discuss any possible risks associated with their findings or present both sides equally when discussing them. Finally, there is some promotional content included in the article which could be seen as biased towards certain conclusions or interpretations made by the authors.
In conclusion, while this article provides an interesting overview on developing machine learning models for predicting shear strength (Vs) of FRP reinforced concrete beams with transverse reinforcement, it could benefit from further exploration into counterarguments or alternative perspectives that may exist within previous studies related to this topic as well as more discussion on potential biases or sources for these biases that may exist within its data or methods. Additionally, it would also benefit from more discussion on possible risks associated with its findings as well as presenting both sides equally when discussing them. Finally, it should also avoid